Difference between revisions of "Sequence Alignments HEXA"

From Bioinformatikpedia
(→‎Statistic results)
(→‎Statistic results)
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'''Distribution of the sequence identity and the e-value'''
  
'''Distribution of the sequence identity and the e-value'''
   
−
The following plots show the distribution of the sequence idetities and the e-values of all used methods. Both values (x-axis) and their frequencies (y-axis) were extractet from the corresponding output-files.
+
The following plots show the distribution of the sequence identities and the e-values of all used methods. Both values (x-axis) and their frequencies (y-axis) were extractet from the corresponding output-files.
   
−
The first image shows the distribution of the sequence identities. The first plot is the distribution for BLAST. Here could be seen that these identity-distribution is very balanced which means the low identities are approximate same common as the very high ones. The same goes for the HHSearch-plot. Contrary the Fasta-distribution has many high frequency for very small identities. This means that FASTA aligns many sequences although they have only a small sequence identity. This could explain why FASTA receives so many hits which do not agree with the other sequence searching tools (see Venn diagramm).
 +
The first image shows the distribution of the sequence identities. The first plot is the distribution for BLAST. Here could be seen that these identity-distribution is very balanced which means the low identities are approximate same common as the very high ones. It is also the same for the HHSearch-plot. Contrary the FASTA-distribution has many high frequency for very small identities. This means that FASTA aligns many sequences although they have only a small sequence identity. This could explain why FASTA receives so many hits which do not agree with the other sequence searching tools (see Venn diagramm).
−
The last four plots represent the corresponding distribution for the different PsiBlasts which were very similar. This is another in indication that PsiBlast received very similar results for the different parameters. Their distribution is also very balanced. There are high frequencies for small identities, middle identities and for high identities.
+
The last four plots represent the corresponding distribution for the different PsiBlasts which were very similar. This is another indication that PsiBlast received very similar results for different parameters. Their distribution is also very balanced. There are high frequencies for small identities, middle identities and for high identities.
   
−
The second image shows the distribution of the e-values. The e-value is a measurement for the probability that a hit is resulting by chance. Therefore the smaller the e-value the better the alignment. All plots except the one for FASTA have high frequencies for small e-values whereby BLAST receives the smalles e-values. The e-values of FASTA have range from 0 to 8 where in contrast the other methods have no evalue higher than 1. Furthermore the highest BLAST e-value is at about 1e-29 which is still very low. In summary this shows again that BLAST deliver the best results and FASTA the worst ones.
+
The second image shows the distribution of the e-values. The e-value is a measurement for the probability that a hit is resulting by chance. Therefore the smaller the e-value the better the alignment. All plots except the one for FASTA have high frequencies for small e-values whereby BLAST receives the smallest e-values. The e-values of FASTA have range from 0 to 8 where in contrast the other methods have no evalue higher than 1. Furthermore the highest BLAST e-value is at about 1e-29 which is still very low. In summary this shows again that BLAST delivers the best results and FASTA the worst ones.
   
 
{| class="centered"
  
{| class="centered"
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'''True positive hits'''
  
'''True positive hits'''
   
−
HSSP (Homology-derived Secondary Structure of Proteins) lists proteins which are homologue and have a similar secondary structure. Therefore we use the HSSP alignment to check our results. Therefore we check how much overlap is between HSSP and the other methods. The overlapping sequences are the true positives.
 +
HSSP (Homology-derived Secondary Structure of Proteins) lists proteins which are homologue and have a similar secondary structure. Therefore we use the HSSP alignment to check our results. The overlapping sequences are the true positives. FASTA has a greater overlap then the other methods (about 10 sequences more). The BLAST result and the results of the Blast variantes (PsiBlast runs) show very similar overlap.
  +
Therefore, in this case FASTA gave the most true positives (although Fasta has also a hugh number of false positive predictions).
  +
   
 
{|
  
{|

Revision as of 19:21, 23 May 2011

Sequence Searches

Use of database searching tools

  • FASTA

/bin/fasta36 seq.fasta /data/blast/nr/nr > fasta_out.txt

  • BLAST

blastall -p blastp -d /data/blast/nr/nr -i mult_seq.fasta > blast_out.txt

  • PSIBLAST

blastpgn -i seq.fasta -j <#iterations> -h <e-value threshold> -d /data/blast/nr/nr > psiblast_out.txt

  • HHSearch

For the HHSearch tool we used the online server for HHSearch.

Statistic results

For the statistical analysis we wrote a script which shows the distribution of the E-Value and the Identity as well as the different aligned sequences. Furthermore, we created a Venn diagram to presentate the overlap between the results of the different searching methods (with http://bioinfogp.cnb.csic.es/tools/venny/index.html). First, we compared the methods BLAST, FASTA and PsiBlast(PsiBlast with 3 and 5 runs and E-Value cutoff 10E-6). Then we looked for the overlap of all done PsiBlasts.


Overlap of the aligned sequences

FASTA found a large number of matches which are not found by the other methods. By comparison the number of hits which were not found by BLAST or PsiBlast, is about 1400. This is much higher than the number of sequences which is found by FASTA and BLAST together. This leads to the conclusion that FASTA aligns many sequences which are probably less good or even wrong. The both different PsiBlast-variants deliver the same hits which are all also found by FASTA. Furthermore all resulting sequences by BLAST were also aligned by FASTA and the most of them are also by PsiBlast. Besides, we decided to compare different runs of PsiBlast. We compared PsiBlast with 3 iterations and an e-Value Cutoff of 0.005 and 10E-6 and also two PsiBlast runs with 5 iterations and the same two e-Value cutoffs as before. In this Vann-Digramm could be seen that the result overlap mostly. Only a few ones differ from the other. This leads to the fact, that PsiBlast with different iteration number and e-value deliver usually a similar result. In summary the BLAST-methods agree with each other. In contrast the FASTA-method delivers much more sequences which do not correspond to the other methods.


Overlap of results from BLAST, FASTA and PsiBlast. Declaration: Psiblast #3: 3 Iterations, E-Value Cutoff: 10E-6; Psiblast #5: 5 Iterations, E-Value Cutoff, 10E-6
Overlap of the results from different PsiBlast Declaration: Psiblast 1: 3 Iterations, E-Value Cutoff: 0.005; Psiblast 2: 5 Iterations, E-Value Cutoff: 0.005 Psiblast 3: 3 Iterations, E-Value Cutoff: 10E-6; Psiblast 4: 5 Iterations, E-Value Cutoff, 10E-6

Distribution of the sequence identity and the e-value

The following plots show the distribution of the sequence identities and the e-values of all used methods. Both values (x-axis) and their frequencies (y-axis) were extractet from the corresponding output-files.

The first image shows the distribution of the sequence identities. The first plot is the distribution for BLAST. Here could be seen that these identity-distribution is very balanced which means the low identities are approximate same common as the very high ones. It is also the same for the HHSearch-plot. Contrary the FASTA-distribution has many high frequency for very small identities. This means that FASTA aligns many sequences although they have only a small sequence identity. This could explain why FASTA receives so many hits which do not agree with the other sequence searching tools (see Venn diagramm). The last four plots represent the corresponding distribution for the different PsiBlasts which were very similar. This is another indication that PsiBlast received very similar results for different parameters. Their distribution is also very balanced. There are high frequencies for small identities, middle identities and for high identities.

The second image shows the distribution of the e-values. The e-value is a measurement for the probability that a hit is resulting by chance. Therefore the smaller the e-value the better the alignment. All plots except the one for FASTA have high frequencies for small e-values whereby BLAST receives the smallest e-values. The e-values of FASTA have range from 0 to 8 where in contrast the other methods have no evalue higher than 1. Furthermore the highest BLAST e-value is at about 1e-29 which is still very low. In summary this shows again that BLAST delivers the best results and FASTA the worst ones.

Distribution of the sequence identities of the different methods
Distribution of the e-values of the different methods



True positive hits

HSSP (Homology-derived Secondary Structure of Proteins) lists proteins which are homologue and have a similar secondary structure. Therefore we use the HSSP alignment to check our results. The overlapping sequences are the true positives. FASTA has a greater overlap then the other methods (about 10 sequences more). The BLAST result and the results of the Blast variantes (PsiBlast runs) show very similar overlap. Therefore, in this case FASTA gave the most true positives (although Fasta has also a hugh number of false positive predictions).


Overlap between HSSP and FASTA
Overlap between HSSP and Blast
Overlap between HSSP and PsiBlast with 3 Iterations and 10E-6 cutoff
Overlap between HSSP and PsiBlast with 5 Iterations and 10E-6 cutoff
Overlap between HSSP and PsiBlast with 3 Iterations and 0.005 cutoff
Overlap between HSSP and psiBlast with 5 Iterations and 0.005 cutoff


With the results of these analysis, we created our file for the multiple alignments.

SeqIdentifier Seq Identity source
99%-90% Sequence Identity
109157872|pdb|2GK1 99% Blast
179460|gb|AAA51827.1 99% Blast
194375013|dbj|BAG62619.1 97% Blast
296213630|ref|XP_002753354.1 95.1% Fasta
297296816|ref|XP_002804897.1 93% Blast
89%-60% Sequence Identity
149692271|ref|XP_001494361.1 85% Blast
187607461|ref|NP_001119815.1 84.3% Fasta
67514549|ref|NP_034551.2 84.1% Fasta
74213671|dbj|BAE35636.1 84% Blast
178056464|ref|NP_001116693.1 83.2% Fasta
59%-40% Sequence Identity
187608414|ref|NP_001120459.1 58.3% Fasta
213513173|ref|NP_001133930.1 57.0% Fasta
38492599|pdb|1O7A 56% Blast
867691|gb|AAA68620.1 55% PsiBlast, 3 Iterations, E-Value Cutoff = 0.005
189239563|ref|XP_975660.2 47% Blast
39%-20% Sequence Identity
299139410|ref|ZP_07032585.1 36% PsiBlast, 3 Iterations, E-Value Cutoff = 0.005
281209747|gb|EFA83915.1 33% Blast
166159759|gb|ABY83272.1 32% PsiBlast, 5 Iterations, E-Value Cutoff = 0.005
251836937|pdb|3GH4 26.4% Fasta
212691177|ref|ZP_03299305.1 22% PsiBlast, 3 Iterations, E-Value Cutoff = 10E-6

Multiple Alignments

  • Cobalt

For coblat it was first necessary to install the programm on our virtual box: Download Cobalt from ftp://ftp.ncbi.nlm.nih.gov/pub/cobalt/executables/2.0.1/ (ncbi-cobalt-2.0.1-x64-linux.tar). Uncompress the archive file with tar xfz ncbi-cobalt-2.0.1-x64-linux.tar and change directory to the uncompressed cobalt directoy.

Call: ./cobalt -i mult_seq.fasta -norps T > cobalt_out.aln

  • ClustalW

clustalw -infile=mult_seq.fasta > clustalW_out.aln

  • Muscle

muscle -in mult_seq.fasta -out muscle_out.aln -clw

  • T-Coffee

t_coffee -seq mult_seq.fasta

  • T-Coffee (3D)

t_coffee -seq mult_seq.fasta -mode expresso


Conservation

Alignment methods Conserved Columns
Gaps 100% cons >90% cons >80% cons >70% cons >60% cons >50% cons >40% cons
Cobalt 384 31 68 75 87 76 0 65
ClustalW 346 29 61 81 84 71 0 65
Muscle 463 32 70 74 84 76 0 74
T-Coffee 609 31 67 74 90 73 0 70
3D T-Coffee 533 32 64 77 89 74 0 70


Gaps

Secondary structure alignment for the protein Hexosaminidase A (http://www.pdb.org/pdb/explore/remediatedChain.do?structureId=2GJX&chainId=A)

For the identification of gaps in secundary structure elements we write a script which comparse the alignment sequence of hexasaminidase with the secondary struture sequence from PDB (http://www.pdb.org/pdb/explore/sequenceText.do?structureId=2GJX&chainId=A). In the following table are the results for all multiple alignment tools.

Alignment methods Gaps in Secundary Structure Elements
Sum of Gaps Helix Extended Coil
Cobalt 384 5 5 1
ClustalW 346 2 0 0
Muscle 463 3 4 1
T-Coffee 609 4 7 4
3D T-Coffee 533 5 4 2


Functional residues

We found several functional residues from (LINK FEHLT NOCH). Because these residues are functionally important, these residues should be conserved. We compared the different alignments and looked if these residues are conserved.

Amino acids Methods
residue position Cobalt ClustalW Muscle T-Coffee 3D T-Coffee
R 178 conserved conserved conserved conserved conserved
D 207 conserved (once E) conserved (once E) conserved (once E) conserved (once E) conserved (once E)
H 262 conserved conserved conserved conserved conserved
D 322 conserved conserved conserved conserved conserved
E 323 conserved conserved conserved conserved conserved
W 373 conserved conserved (once V, R) conserved conserved conserved
W 392 conserved conserved (once P, T, G) conserved conserved conserved
Y 421 conserved conserved (twice G, once -, S) conserved conserved conserved (once H)
N 423 non-conserved non-conserved non-conserved non-conserved non-conserved
W 460 conserved (once -) conserved (once -) conserved (once -) conserved (once -) conserved (once -)
E 462 conserved (once -) conserved (once Q) conserved (once Q) conserved (once -) conserved (once -)
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